






'''simple docstring'''







import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )
  # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
  parser.add_argument(
      "--original_config_file",
      default=None,
      type=str,
      help="The YAML config file corresponding to the original architecture.",
  )
  parser.add_argument(
      "--num_in_channels",
      default=None,
      type=int,
      help="The number of input channels. If `None` number of input channels will be automatically inferred.",
  )
  parser.add_argument(
      "--scheduler_type",
      default="pndm",
      type=str,
      help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
  )
  parser.add_argument(
      "--pipeline_type",
      default=None,
      type=str,
      help=(
          "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
          ". If `None` pipeline will be automatically inferred."
      ),
  )
  parser.add_argument(
      "--image_size",
      default=None,
      type=int,
      help=(
          "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
          " Base. Use 768 for Stable Diffusion v2."
      ),
  )
  parser.add_argument(
      "--prediction_type",
      default=None,
      type=str,
      help=(
          "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
          " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
      ),
  )
  parser.add_argument(
      "--extract_ema",
      action="store_true",
      help=(
          "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
          " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
          " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
      ),
  )
  parser.add_argument(
      "--upcast_attention",
      action="store_true",
      help=(
          "Whether the attention computation should always be upcasted. This is necessary when running stable"
          " diffusion 2.1."
      ),
  )
  parser.add_argument(
      "--from_safetensors",
      action="store_true",
      help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
  )
  parser.add_argument(
      "--to_safetensors",
      action="store_true",
      help="Whether to store pipeline in safetensors format or not.",
  )
  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
  parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
  parser.add_argument(
      "--stable_unclip",
      type=str,
      default=None,
      required=False,
      help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
  )
  parser.add_argument(
      "--stable_unclip_prior",
      type=str,
      default=None,
      required=False,
      help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
  )
  parser.add_argument(
      "--clip_stats_path",
      type=str,
      help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
      required=False,
  )
  parser.add_argument(
      "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
  )
  parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
  parser.add_argument(
      "--vae_path",
      type=str,
      default=None,
      required=False,
      help="Set to a path, hub id to an already converted vae to not convert it again.",
  )
  __lowerCAmelCase        =							parser.parse_args()

  __lowerCAmelCase        =							download_from_original_stable_diffusion_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      prediction_type=args.prediction_type,
      model_type=args.pipeline_type,
      extract_ema=args.extract_ema,
      scheduler_type=args.scheduler_type,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      stable_unclip=args.stable_unclip,
      stable_unclip_prior=args.stable_unclip_prior,
      clip_stats_path=args.clip_stats_path,
      controlnet=args.controlnet,
      vae_path=args.vae_path,
  )

  if args.half:
    pipe.to(torch_dtype=torch.floataa)

  if args.controlnet:
    # only save the controlnet model
    pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
  else:
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)












'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




